@article{(Open Science Index):https://publications.waset.org/pdf/2535,
	  title     = {An Optimal Feature Subset Selection for Leaf Analysis},
	  author    = {N. Valliammal and  S.N. Geethalakshmi},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {2},
	  year      = {2012},
	  pages     = {191 - 196},
	  ee        = {https://publications.waset.org/pdf/2535},
	  url   	= {https://publications.waset.org/vol/62},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 62, 2012},
	}